A comparative study of Neural networks architectures on Arabic text categorization using feature extraction

• Published in: 2010 International Conference on Machine and Web Intelligence pp. 102 - 107
• Main Authors: Harrag, F, Al-Salman, Abdul Malik Salman, BenMohammed, M
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2010
• Subjects: Accuracy; Arabic Text Categorization; Artificial neural networks; Computational modeling; Matrix decomposition; MLP; Natural Language Processing; Neural Network; RBF; Singular Value Decomposition; Support vector machine classification; Text categorization; Training
• ISBN: 9781424486083; 1424486084
• DOI: 10.1109/ICMWI.2010.5648051

In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness of the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) classifiers are implemented. Experiments are conducted using an in-house corpus of Arabic texts. Precision, recall and F-measure are used to quantify categorization effectiveness. The results show that the proposed SVD-Supported MLP/RBF ANN classifier is able to achieve high effectiveness. Experimental results also show that the MLP classifier outperforms the RBF classifier and that the SVD-supported NN classifier is better than the basic NN, as far as Arabic text categorization is concerned.